EAGER: A Study of the Limitations of Program Analysis for Autovectorization

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

Most modern microprocessors support some form of vector operations that allow the same operation to be applied to small vectors of arguments simultaneously. Studies have shown that use of these instructions can improve the performance of many scientific codes by a factor of 2 or more. Unfortunately, the state of the art in autovectorization falls far short of this goal, only achieving improvements of 20-30% on the same codes. While studies have shown that current autovectorizing compilers do not identify all of the opportunities for vectorization, little is known about why they fail to do so. Specifically, the project will focus on identifying weaknesses in compiler analyses that cause failures to vectorize. The goal of this research is to identify a list of causes that are responsible for the vast majority of these failures. This research will make it possible to develop better compiler analysis algorithms that will result in better autovectorizing compilers. The performance benefits of such compilers will improve the performance of applications ranging from multimedia software to scientific computing.